2026 Intern, PhD, Machine Learning Engineer, Simulation
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The role
Research, implement, and evaluate state-of-the-art generative models and advanced sampling techniques for ultra-realistic multi-agent simulations and full fidelity scenario generation.
Collaborate with research and engineering teams to integrate models into simulation and verification workflows, applying cutting-edge vision-language and efficiency techniques.
Contribute to projects that improve world modeling of high-fidelity sensor data for an E2E AI-first system, with opportunities to publish novel research.
Currently pursuing a Ph.D. in Computer Science, ML, Electrical Engineering, or a related field, with a strong publication record.
Hands-on experience with deep learning frameworks (PyTorch, TensorFlow, JAX) and proficiency in Python and/or C/C++.
Preferences include large-scale model training, 3D vision (Gaussian Splatting, NeRF), and 3D asset generation, plus experience with TPU/XLA and open-source contributions.
Collaborate with research and engineering teams to integrate models into simulation and verification workflows, applying cutting-edge vision-language and efficiency techniques.
Contribute to projects that improve world modeling of high-fidelity sensor data for an E2E AI-first system, with opportunities to publish novel research.
Currently pursuing a Ph.D. in Computer Science, ML, Electrical Engineering, or a related field, with a strong publication record.
Hands-on experience with deep learning frameworks (PyTorch, TensorFlow, JAX) and proficiency in Python and/or C/C++.
Preferences include large-scale model training, 3D vision (Gaussian Splatting, NeRF), and 3D asset generation, plus experience with TPU/XLA and open-source contributions.
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